This paper presents a method for pedestrian activity classification and gait analysis based on the microelectromechanical-systems inertial measurement unit (IMU). The work targets two groups of applications, including the following: 1) human activity classification and 2) joint human activity and gait-phase classification. In the latter case, the gait phase is defined as a substate of a specific gait cycle, i.e., the states of the body between the stance and swing phases. We model the pedestrian motion with a continuous hidden Markov model (HMM) in which the output density functions are assumed to be Gaussian mixture models. For the joint activity and gait-phase classification, motivated by the cyclical nature of the IMU measurements, each individual activity is modeled by a "circular HMM." For both the proposed classification methods, proper feature vectors are extracted from the IMU measurements. In this paper, we report the results of conducted experiments where the IMU was mounted on the humans' chests. This permits the potential application of the current study in camera-aided inertial navigation for positioning and personal assistance for future research works. Five classes of activity, including walking, running, going upstairs, going downstairs, and standing, are considered in the experiments. The performance of the proposed methods is illustrated in various ways, and as an objective measure, the confusion matrix is computed and reported. The achieved relative figure of merits using the collected data validates the reliability of the proposed methods for the desired applications. / <p>QC 20130114</p>

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Safety is considered as one of the most important areas in future research and development within the automotive industry. New functionality, such as driver support and active/passive safety systems are examples where development mainly focuses on safety. At the same time, the trend is towards more complex systems, increased software dependence and an increasing amount of sensors and actuators, resulting in a higher risk associated with software and hardware failures. In the area of functional safety, standards such as ISO 26262 assess safety mainly focusing on qualitative assessment techniques, whereas usage of quantitative techniques is a growing area in academic research. This thesis considers the field functional safety, with the emphasis on how hardware and software failure probabilities can be used to quantitatively assess safety of a system/function. More specifically, this thesis presents a method for quantitative safety assessment using Bayesian networks for probabilistic modeling. Since the safety standard ISO 26262 is becoming common in the automotive industry, the developed method is adjusted to use information gathered when implementing this standard. Continuing the discussion about safety, a method for modeling faults and failures using Markov models is presented. These models connect to the previous developed Bayesian network and complete the quantitative safety assessment. Furthermore, the potential for implementing the discussed models in the Modelica language is investigated, aiming to find out if models such as these could be useful in practice to simplify design work, in order to meet future safety goals.

This thesis introduces new techniques for finding genes in genomic sequences. Genes are regions of a genome encoding proteins of an organism. Identification of genes in a genome is an important step in the annotation process after a new genome is sequenced. The prediction accuracy of gene finding can be greatly improved by using experimental evidence. This evidence includes homologies between the genome and databases of known proteins, or evolutionary conservation of genomic sequence in different species. <br /><br /> We propose a flexible framework to incorporate several different sources of such evidence into a gene finder based on a hidden Markov model. Various sources of evidence are expressed as partial probabilistic statements about the annotation of positions in the sequence, and these are combined with the hidden Markov model to obtain the final gene prediction. The opportunity to use partial statements allows us to handle missing information transparently and to cope with the heterogeneous character of individual sources of evidence. On the other hand, this feature makes the combination step more difficult. We present a new method for combining partial probabilistic statements and prove that it is an extension of existing methods for combining complete probability statements. We evaluate the performance of our system and its individual components on data from the human and fruit fly genomes. <br /><br /> The use of sequence evolutionary conservation as a source of evidence in gene finding requires efficient and sensitive tools for finding similar regions in very long sequences. We present a method for improving the sensitivity of existing tools for this task by careful modeling of sequence properties. In particular, we build a hidden Markov model representing a typical homology between two protein coding regions and then use this model to optimize a component of a heuristic algorithm called a spaced seed. The seeds that we discover significantly improve the accuracy and running time of similarity search in protein coding regions, and are directly applicable to our gene finder.

Scalable video coding (SVC) supports full scalability by extracting a partial bitstream to adapt to transmission and display requirements in multimedia applications. Most conventional video content is stored in non-scalable format, e.g., H.264/AVC, necessitating the development of an efficient video transcoding from a conventional format to a scalable one. This work describes a fast video transcoding architecture that overcomes the complexity of different coding structures between H.264/AVC and SVC. The proposed algorithm simplifies the mode decision process in SVC owing to its heavy computations. The current mode in SVC is selected by the highest conditional probability of SVC¡¦s mode given the H.264/AVC¡¦s mode. Exactly when an error prediction occurs is then detected using Bayesian theorem, followed by its refinement using the Markov model. Experimental results indicate that the proposed algorithm saves on average 75.28% of coding time with 0.13 dB PSNR loss over that when using a cascaded pixel domain transcoder.

In this thesis, a Mandarin toponym speech recognition system is developed using MFCC, LPC and HMM under Red Hat Linux 9.0. The system is based on monosyllable HMM's to select the initial toponym candidates, and its final classification result can be obtained by further pitch identification mechanisms. For speaker-dependent case, a 90% correct rate can be achieved approximately and the recognition process can be accomplished within 1.5 seconds on the average.

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There are a lot of difficulties that have to be overcome in the speaker-independent (S.I.) phrase recognition system . And the feasibility of accurate ,real-time and robust system pose of the greatest challenges in the system. In this thesis ,the speaker-independent phase recognition system is based on Hidden Markov Model (HMM). HMM has been proved to be of great value in many applications, notably in speech recognition. HMM is a stochastic approach which characterizes many of the variability in speech signal. It applys the state-of-the-art approach to Automatic Speech Recognition .

A design of speech recognition system for Chinese names has been established in this thesis. By identifying surname first, that is an unique feature of the Chinese names, the classification accuracy and computational time of the system can be greatly improved. This research is primarily based on hidden Markov model (HMM), a technique that is widely used in speech recognition. HMM is a doubly stochastic process describing the ways of pronumciation by recording the state transitions according to the time-varing properties of the speech signal. The results of the HMM are compared with those of the segmental probability model (SPM) to figure out better option in recognizing base-syllables. Under the conditions of equal segments, SPM not only suits Mandarin base-syllable structure, but also achieves the goal of simplifying system since it does not need to find the best transformation of the utterance. A speaker-independent 3000 Chinese names recognition system has been implemented based on the Mandarin microphone database recorded in the laboratory environment.

We build a two-pass Mandarin Automatic Speech Recognition (ASR) decoder on mobile device (PDA). The first-pass recognizing base syllable is implemented by discrete Hidden Markov Model (HMM) with time-synchronous, tree-lexicon Viterbi search. The second-pass dealing with language model, pronunciation lexicon and N-best syllable hypotheses from first-pass is implemented by Weighted Finite State Transducer (WFST). The best word sequence is obtained by shortest path algorithms over the composition result. This system limits the application in travel domain and it decouples the application of acoustic model and the application of language model into independent recognition passes. We report the real-time recognition performance performed on ASUS P565 with a 800MHz processor, 128MB RAM running Microsoft Window Mobile 6 operating system. The 26-hour TCC-300 speech data is used to train 151 acoustic model. The 3-minute speech data recorded by reading the travel-domain transcriptions is used as the testing set for evaluating the performances (syllable, character accuracies) and real-time factors on PC and on PDA. The trained bi-gram model with 3500-word from BTEC corpus is used in second-pass. In the first-pass, the best syllable accuracy is 38.8% given 30-best syllable hypotheses using continuous HMM and 26-dimension feature. Under the above syllable hypotheses and acoustic model, we obtain 27.6% character accuracy on PC after the second-pass.